Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.

Optimization-based strategies to solve problems in production scheduling have been extensively used in the last decades due to their generality, flexibility and potential to find the best solutions in terms of costs, customer satisfaction, and efficiency. Traditionally, most efforts have been directed towards the development of mathematical models that are computationally tractable. However, the effective solution of large-scale scheduling models remains nontrivial. The main objective of this thesis is the development of solution methods for the different types of chemical plants. Our discussion is largely motivated by a new approach to the analysis of timing and inventory restrictions in scheduling problems. First, we propose a family of algorithms that are suitable for maximization problems in network environments. By preprocessing the original data we calculate parameters that are used to develop tightening constraints. We also introduce the concept of variable start and finish times and derive expressions to relate them and connect them with original decision variables. By means of computational experiments we show the effectiveness of these methods in improving the solution process of optimization-based models for scheduling. Second, we develop a new family of discrete-time models for sequential environments. Almost all the existing models in the literature use a continuous representation of time. We discuss the advantages of discrete-time models and propose different solution methods to improve their computational performance. A computational study is included to test the improvements and compare with existing approaches. Significant reduction in computational time and optimality gap is achieved. Third, we extend methods based on reformulations and tightening constraints from discrete-time to continuous time models in network environments. We use specific characteristics of the latter to improve computational performance, testing our methods on several benchmark instances. Finally we test the proposed methods on large-scale instances for which optimal solutions had not been found before or whose computational performances demanded long solution times. This way we show that our formulations and methods improve the tractability of industrial-scale instances. Optimal or near-optimal solutions are now accessible in reasonable time for many cases for which only suboptimal solutions from heuristics procedures or empirical methods were available.

Chemical production scheduling optimization has the potential to reduce operating cost, increase profits, and improve efficiency. These optimization problems often formulated as mixed integer programming models which, despite advances in computer hardware and optimization software, remain hard to solve. We first formulate a more general model and then develop several solution methods to speed up the computational times. We show that the production environment can be defined by material handling constraints and formulate a general model that is valid for all production environments. We develop new formulations for processes with changeovers and compare their relative tightness and present computational results for several example problems. In the first solution method, customer orders are propagated backwards through the network to find the minimum amount of material each task must process, providing a lower bound on the number of times each task must run. We extend these methods to the general model. This method is most effective for cost minimization and can lead to a 2-3 order-of-magnitude improvement in computational time. The next method reduces the size of the model by using different time grids for each task, unit, material, and utility. We prove that this formulation will have the same optimal solution as a single-grid formulation. This method is most effective for makespan. The third method uses a parallel batch-and-bound algorithm. The scheduling problem is divided into subproblems by branching on the number of times each task runs. Each of these subproblems is solved in parallel by a separate core and may be divided further. Many difficult problems can be solved to optimality with this method. The final method is the simplest and most effective. Many equivalent schedules can be formed by simply shifting tasks in units with idle time earlier or later. These schedules have the same number of batches and similar objectives. Introducing a new integer variable representing the number of batches of each task allows the solver to branch on this variable to find truly different schedules quickly. This method is the most effective with over 2, 3, or 4 orders-of-magnitude improvements for makespan, profit, and cost optimization respectively.

Optimization-based chemical production scheduling allows for efficient utilization of available assets and brings significant operational benefits including reduction in costs. Unfortunately, application of such techniques to industrial settings is challenging due to multiple reasons: (i) the optimization models need to be general to accommodate different production processes, (ii) the solution of such models need to be quick to allow for frequent updates to the schedules, and (iii) the models should be capable of providing multiple alternative schedules for the practitioners to compare and implement. The goal of this work is to address the aforementioned challenges and bring optimization-based scheduling techniques closer to industrial applications. First, we develop mathematical programming models for simultaneous batching and scheduling in general sequential production environment while taking into account various process features including storage policies and limited shared utilities. The models are based on novel modeling approaches which allow for exploitation of instance characteristics, thus leading to solution of large-scale instances. Second, we develop a novel framework for a solution algorithm that harnesses the advantages of discrete- and continuous-time scheduling models. Specifically, we propose an algorithm that has modeling flexibility and computational efficiency of discrete-time models, as well as high solution accuracy of their continuous counterparts. We investigate in detail how the algorithm can be improved and extended to solve real-world industrial instances that are thought to be computationally near impossible if transitional methods were to be used. Finally, we develop systematic methods to generate multiple alternative schedules, specifically to account for modeling simplifications introduced in the scheduling models and plant nervousness when revising schedules. We generate alternative schedules by quantifying specific characteristics of a schedule using explicitly defined metrics, which are favored at different degrees by penalizing them in the objective function with varying penalty weights. We show that, by leveraging penalty weights, schedules with desirable properties can be readily found.

This book presents a number of efficient techniques for solving large-scale production scheduling and planning problems in process industries. The main content is supplemented by a wealth of illustrations, while case studies on large-scale industrial applications, ranging from continuous to semicontinuous and batch processes, round out the coverage. The book examines a variety of complex, real-world problems, and demonstrates solutions that are applicable to scenarios and countries around the world. Specifically, these case studies include: • the production planning of the bottling stage of a major brewery at the Cervecería Cuauhtémoc Moctezuma (Heineken Int) in Mexico;• the production scheduling for multi-stage semicontinuous processes at an ice-cream production facility of Unilever in the Netherlands;• the resource-constrained production planning for the yogurt production line at the KRI KRI dairy production facility in Greece; and• the production scheduling for large-scale, multi-stage batch processes at a pharmaceutical batch plant in Germany. In addition, the book includes industrial-inspired case studies of: • the simultaneous planning of production and logistics operations considering multi-site facilities for semicontinuous processes; and• the integrated planning of production and utility systems in process industries under uncertainty. Solving Large-scale Production Scheduling and Planning in the Process Industries offers a valuable reference guide for researchers and decision-makers alike, as it shows readers how to evaluate and improve existing installations, and how to design new ones. It is also well suited as a textbook for advanced courses on production scheduling and planning in industry, as it addresses the optimization of production and logistics operations in real-world process industries.